Seismic random noise attenuation using structure-oriented 3D curvelet transform

Abstract

Sparse constraints based on curvelet transform have been widely applied in seismic noise suppression. Traditional schemes usually adopt global or multi-scale thresholding to constrain the coefficients corresponding to noise, which may ignore the structural characteristics and result in signal distortion. It is usually challenging to balance noise suppression and signal preservation. To address this problem, we develop a structure-oriented 3D denoising method based on the 3D curvelet transform, which uses dip information to analyze the complexity of local features. The non-stationary thresholding scheme based on local complexity is implemented, providing strong constraints for low-complexity data to suppress noise and weak constraints for high-complexity data to protect signal. Furthermore, the coarse scale coefficients are insensitive to noise interference; only the fine-scales coefficients are constrained in our proposed scheme. Numerical tests on synthetic and field data demonstrate that the proposed method has better denoising performance for large dip feature data than the conventional global and multi-scale thresholding schemes.